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A mixture model approach to detecting differentially expressed genes with microarray data

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Abstract

An exciting biological advancement over the past few years is the use of microarray technologies to measure simultaneously the expression levels of thousands of genes. The bottleneck now is how to extract useful information from the resulting large amounts of data. An important and common task in analyzing microarray data is to identify genes with altered expression under two experimental conditions. We propose a nonparametric statistical approach, called the mixture model method (MMM), to handle the problem when there are a small number of replicates under each experimental condition. Specifically, we propose estimating the distributions of a t -type test statistic and its null statistic using finite normal mixture models. A comparison of these two distributions by means of a likelihood ratio test, or simply using the tail distribution of the null statistic, can identify genes with significantly changed expression. Several methods are proposed to effectively control the false positives. The methodology is applied to a data set containing expression levels of 1,176 genes of rats with and without pneumococcal middle ear infection.

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Acknowledgements

W.P. was supported by an NIH grant (R01-HL65462) and a Minnesota Medical Foundation grant. The authors are grateful to two referees for many helpful comments and suggestions.

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Authors and Affiliations

  1. Division of Biostatistics, School of Public Health, University of Minnesota, A460 Mayo, MMC 303, 420 Delaware Street SE, Minneapolis, MN 55455–0378, USA

    Wei Pan & Chap T. Le

  2. Department of Otolaryngology, School of Medicine, University of Minnesota, Minneapolis, MN 55455-0378, USA

    Jizhen Lin

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  1. Wei Pan
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Correspondence to Wei Pan.

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Pan, W., Lin, J. & Le, C.T. A mixture model approach to detecting differentially expressed genes with microarray data. Funct Integr Genomics 3, 117–124 (2003). https://doi.org/10.1007/s10142-003-0085-7

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